Small-sided games (SGs) are being increasingly used by coaches in the context of team sports (19). The assumed objective of SGs is to improve technical and tactical skills (27,35) or the overall fitness of players (21,24). As a result of the growing interest in SGs, numerous studies have examined the effect of manipulating different variables in various sports, including rugby (15,18), basketball (38), and tennis (34). As regards soccer, a large body of research has focused on these kinds of training regimes to study physiological workload, perceptual demands, physical load, and technical and tactical requirements (22).
The principle of specificity justifies the use of these kinds of drills in training (36), because it is assumed that performance improves more when training simulates the physiological demands and movement patterns of competitive matches (38). It is common practice in professional and semiprofessional soccer to have SGs and weekly friendly matches (FMs) during the training week (1). This is done with the attempt to promote individual and team fitness and skill development (1). However, to the best of this study authors' knowledge, comparison between activity profiles of SGs and FMs was only performed in women's soccer (19).
In the aforementioned study, the authors concluded that SGs simulate most of the features of actual matches but were insufficient to reproduce the high-intensity and the repeated-sprint demands of competitive situations. Although these findings are yet to be replicated in male semiprofessional soccer players, they do appear to be corroborated by research in other team sports such as rugby (17) or basketball (28). Information about the possible differences between SGs and FMs would result in great interest for training prescription optimization for coaches and strength and conditioning professionals dealing with men's semiprofessional soccer.
Given the above, the aim of this study was to compare the physical demands of actual matches with those of SG in male semiprofessional soccer players. With this objective, we used different physical variables and load indicators with special attention paid to high-intensity efforts.
Experimental Approach to the Problem
In this study, the construct of SG specificity was challenged comparing its activity profile with that of FMs (19). Comparisons were performed by examining male semiprofessional soccer players during the 2010–2011 competitive seasons. A nonexperimental descriptive comparative design was used to look for differences between SGs and FMs over arbitrarily chosen categories normalized per exercise duration.
As work hypothesis it was assumed that SGs would elicit physical demands similar to those of FMs except for high-intensity efforts. This is because of the difference in playing area dimensions and in time constants between SG and FM conditions.
Activity profiles of SGs and FMs were obtained using global positioning system (GPS) technology, which enables movement patterns in sports to be monitored in a valid and reliable manner (8,26,29). Moreover, the software packages now available offer quick and simple analyses (13), making them practical tools for monitoring the movement of several players at the same time (20).
Twenty-seven semiprofessional male soccer players (Spanish third division) took part in the study (mean ± SD age, 22.8 ± 4.5 years; height, 177 ± 5.3 cm; weight, 74.4 ± 4.8 kg). They were all members of the same team that competes at the regional level, and they had a mean of 12.5 years of experience playing federation soccer. All the players were notified of the research design and its requirements, and the potential benefits and risks, and they all gave their informed consent before the start. The Ethics Committee of the University of the Basque Country gave its institutional approval for the study.
Data were collected using portable GPS devices (MinimaxX ν.4.0, Catapult Innovations) operating at a sampling frequency of 10 Hz. The players wore a special harness that enabled these devices to be fitted to the upper part of their backs. The GPS device was activated 15 minutes before kick-off, in accordance with the manufacturer's instructions. After recording, the data were downloaded to a PC and analyzed using the software package Logan Plus ν.4.4 (Catapult Innovations, 2010). In the weeks before the study, the players were familiarized to the use of these devices and to the SG formats used.
Research has suggested that compared with other methods of analysis, GPS devices underestimate the distance covered at high intensities (33). However, using a higher sampling frequency seems to increase the accuracy of the information provided by these devices (12). In this regard, the reliability, accuracy, and validity of the devices used in this study (with a sampling frequency of 10 Hz) can produce better results (8) than those obtained in previous studies (12,26,29), which used a sampling frequency of 1 and 5 Hz.
The variables used to compare the physical demands of SGs and FMs were as follows: (a) the distance covered per minute (DCm); (b) the distance covered per hour (DCh) and (c) the percentage of time (%T) spent in each of the speed zones: 0–6.9, 7.0–12.9, 13.0–17.9, 18.0–20.9, and >21 km·h−1. These speed and movement zones are similar to those used in other studies (10,11,30). In addition to these variables, we also monitored the maximum speed (Vmax) achieved.
Physical response was also studied by means of several global indicators of workload. The first of these was the work:rest ratio, defined as the distance covered by the player at a speed >4 km·h−1 (period of activity or work) divided by the distance covered at a speed of 0–3.9 km·h−1 (period of recovery or rest). Second, accelerometry was used to estimate the players' workload (9,28). This indicator combines the accelerations produced in 3 planes of body movement, which were measured here using a triaxial 100-Hz accelerometer incorporated within the GPS devices. To avoid bias because of the different durations of SGs and FMs, these values were normalized for each minute of play, as in the study by Montgomery et al. (28). Player workload is a new indicator (28), which seems to be highly correlated with both heart rate and blood lactate levels (unpublished data; Montgomery et al., 2010). This indicator is calculated using the following formula:
where aca is the acceleration along the anteroposterior or horizontal axis, act is the acceleration along the transverse or lateral axis, acv is the acceleration along the vertical axis, i is the current time, and t is time.To further characterize players' playing demands, the exertion index was considered. This is derived from the speed of movements around the playing area and has been studied in the Australian Football League (40). The index is calculated using 3 equations, this being the sum of the weighted instantaneous speed, the weighted accumulated speed over 10 seconds, and the weighted accumulated speed over 60 seconds. The formula used was
V is the speed in kilometers per hour captured at 10 Hz, V10 is the mean speed over 10 seconds, and V60 is the mean speed over 60 seconds.
In line with other previous research (5), we also studied several variables associated with repeated high-intensity efforts (RHIEs). A series of RHIEs is said to be performed when a player makes at least 3 efforts at a speed >13 km·h−1 and with a <21-second recovery between them (39). The variables studied for RHIEs were number of RHIEs, mean and maximum number of efforts per RHIE, mean and maximum duration of efforts made, mean and maximum duration between efforts, and mean and maximum duration between RHIEs. To enable comparisons to be made between SGs and FMs, the frequencies of RHIEs were expressed relative to 1 hour of play.
Match activities were assessed from 7 different 11-a-side FMs. At least 72 hours elapsed between each match, and they were all played at a similar time of the day (18:00 hours), with a temperature of 20°C and relative humidity of 78%. The opposing teams were always of a similar level, and the match format was kept constant so as to reduce any variability in the players' physical performance (30). The system of play used by the observed team in all the FMs was 1–4–4–1–1, comprising 2 central defenders, 2 full backs, 2 central midfielders, 2 wide midfielders, and 1 attacking midfielder playing behind a single center forward. The 7 FMs yielded a total of 27 recordings, equivalent to 54 hours of analysis. Given that injuries and substitutions occur during matches, a recording was only included in the analysis if it lasted at least 15 minutes. The playing area relative to each player was approximately 300 m2 across all FMs, as was the length:width ratio of the pitch (1.45:1).
The SG demands were examined over a 5-week period (from January to February 2010) monitoring 9 training sessions, which were held at least 48 hours apart and at a similar time of the day (20:00 hours). Each training session involved 3 SG formats (n = 27, 3 number of players per team × 3 types of pitch × 3 bouts), with a maximum of 12 players being monitored in each one, thereby producing a total of 217 recordings. This was equivalent to 14.5 hours of analysis. A mean of 8.0 ± 1.9 recordings was taken from each SG. The 3 SG formats were 3 vs. 3, 5 vs. 5, and 7 vs. 7, played on 3 different pitch layouts (without goals, with 2 regulation goals and goalkeepers, and with 2 small goals but no goalkeepers). The playing area relative to each player was maintained constant (210 m2) across all 3 SG formats, as was the length:width ratio of the pitch (1.45:1). All the SGs lasted 4 minutes, with a 2-minute passive rest between repetitions. During rest periods, the players were allowed to drink fluids “at libitum.” All the participants were advised to maintain their normal diet, with special emphasis being placed on a high intake of water and carbohydrates.
The reliability of the variables considered was assessed before this study in the same population studied here as intraclass coefficients (ICC). From the results reported, ICCs are found to range from 0.82 to 0.92.
The data are presented as mean ± SD. The homogeneity of variances was examined by means of Levene's test. The presence of significant differences between FMs and SGs was determined using Student's t-test.
Power calculations showed that to reach a nominal power of 0.80 in most variables considered, 11 subjects were necessary. The effect size statistic (ES) was used to assess the magnitude of difference between SGs and FMs in each physical variable. The criteria for interpreting effect sizes were 0.2 trivial, 0.2–0.6 small, 0.6–1.2 moderate, 1.2–2.0 large, and 2.0 very large (23). Precision of estimation was indicated with a 90% confidence interval. The percent coefficient of variation was used to characterize the degree of variability in the physical data. All the statistical analyses were performed using SPSS16.0 for Windows, with significance being set at p ≤ 0.05.
Table 1 shows the comparison of physical demands for SGs and FMs. The global indicators of workload were significantly higher (p < 0.05) during SGs than during FMs, except for Vmax, which was higher during FMs (p < 0.01).
Figure 1 shows the distance covered per hour of play (DCh) for each of the speed zones studied. The distribution of the distance covered in different speed zones is similar across both formats (SGs and FMs). It can be seen that significant differences (p < 0.01) are only present in 2 speed zones: 7.0–12.9 km·h−1 (SGs > FMs) and >21 km·h−1 (FMs > SGs). The remaining zones show no significant differences, despite the fact that a greater distance was covered during SGs at speeds of 13.0–17.9 km·h−1, whereas in FMs, a greater distance is covered per hour at speeds of 0.0–6.9 and 18.0–20.9 km·h−1.
The percentage of time (%T) spent in each speed zone is shown in Figure 2. As in the case of the distance covered (Figure 1), the distribution of time spent in each speed zone was similar for SGs and FMs. It should be noted, however, that although the greatest distance was covered at speeds of 7.0–12.9 km·h−1, the players in both formats (SGs and FMs) spent most time in the speed zone 0.0–6.9 km·h−1. Furthermore, significant differences (p < 0.01) were observed between the 2 formats in the speed zones 0.0–6.9, >21 km·h−1 (both FMs > SGs) and 7.0–12.9 km·h−1 (SGs > FMs).
Table 2 shows more detailed data for both SGs and FMs regarding the different variables in just 1 speed zone, namely, sprint (>21 km·h−1). During FMs, the players made a significantly (p < 0.01) greater number of sprints per hour of play (15.3 vs. 7.5), with greater mean durations (2.3 vs. 0.7 seconds), mean distances (15.2 vs. 4.4 m), maximum durations (5.1 vs. 0.8 seconds), and maximum distances (34.4 vs. 4.8 m). The frequency of sprints per hour of play was also greater in FMs for all the distance zones established, this difference being significant (p < 0.01) for sprint distances of 10–40 and >40 m (p < 0.01).
Finally, Table 3 shows the results for the variables associated with RHIEs. Significant differences (FMs > SGs) were obtained for most of the variables studied (p < 0.01). The only variables that did not differ significantly between FMs and SGs were the frequency of RHIEs per hour of play, the mean number of efforts per RHIE, and the mean duration between efforts. During FMs, there was a greater number of maximum efforts, a greater mean and maximum duration of efforts, a greater maximum duration between efforts, and a greater mean and maximum duration between RHIEs (all p < 0.01).
This is the first study that compared the activities profile of SGs and FMs in male semiprofessional soccer players. The results showed that the high-intensity profile during FMs was higher than in SGs. This partly supports our original work hypothesis.
A novel approach in this study was to use some indicators to compare the movement patterns of SGs and FMs. The global indicators of workload (work:rest ratio, player workload, and exertion index) were higher for SGs than for FMs, as was the DCm (Table 1). This would seem to indicate that SGs are played at a higher intensity than are FMs. Interestingly, this contrasts with the results of a study of basketball by Montgomery et al. (28), who found that physical load was greater during game play than during practice drills. The present findings regarding the work:rest ratio are, however, in line with those of a previous study of young soccer players (2), which reported a value for this indicator of 3.5 (the same as the mean value obtained here for SGs). As regards the exertion index, this was significantly higher in SGs compared with that in FMs, although the values for both formats were higher than those reported by Wisbey et al. (40) in a study of Australian soccer. These authors reported a number of significant differences in relation to player positions, venues, match quarters, and seasons.
In terms of the distance covered in each of the speed zones, the present results again revealed significant differences between SGs and FMs. The indicator DCm showed that players ran further during SGs and that the mean value obtained (118.3 m·min−1) was similar to that reported in studies of futsal using either 2-dimensional photogrammetry (i.e., 117.3 and 121 m·min−1, respectively) or GPS technology (i.e., 118 m·min−1) (3,4,7). However, the value obtained here is somewhat higher than the 100 m·min−1 reported for Spanish preadolescent players (2). At all events, these comparisons should be made tentatively because the samples in question are not equivalent. The overall distance profile was similar across the exercise modes (i.e., FMs vs. SGs) in all the activities considered but for speeds >21 km·h−1 (FMs > SGs, p < 0.01).
With respect to the percentage of time (%T) spent in each speed zone, results showed that less time was spent in high-intensity running during SGs than during FMs. Similar findings have been reported by Gabbett (17) in a study of hockey, comparing field training with competition. Interestingly, a previous study on female soccer players (19) found no differences in %T when comparing SGs with competitive matches. In this study, the time spent by players at speeds >21 and <6.9 km·h−1 was significantly greater during FMs than during SGs. This finding is of importance because maximum speed and %T spent in this speed zone increases according to competitive level with a more stable intermatch performance (1,30).
In terms of sprinting, the present results show that the Vmax achieved was greater in FMs than in SGs. Furthermore, the sprints produced during FMs were more frequent, of longer duration, and of greater mean and maximum distances. This difference could be obtained because of the different dimensions of the playing surface in SGs, which make it more difficult to reach speeds that might be categorized as sprints (6). At all events, most sprints in both formats (SGs and FMs) were made over a distance of 10–40 m.
This study is the first to compare the repeated-sprint demands (RHIEs) of male soccer during SGs and FMs. The study of RHIEs is of key importance because they are closely related to sports performance (25,32). However, it is unclear whether the most widely used SGs actually reproduce the demands of competitive matches as regards RHIEs. In this study, significant differences were observed (FMs > SGs) for almost all the RHIE variables, the exceptions being the number of RHIEs, the mean number of efforts per RHIE, and the mean duration between efforts. To the best of our knowledge, no previous studies have used these types of indicators to evaluate physical demands, especially for high-intensity efforts. Overall, the literature suggests that SGs do offer a specific method of training and manage to replicate most of the demands of competitive matches (14,16,18,37). However, the findings of this study showed that SGs are limited in relation to specific aspects of fitness, namely, the insufficient stimulation of high-intensity efforts and the small number of repeated sprints. This is so with work to rest times that differ considerably from what occurs in competition.
The main limitation of this study concerns the sample size. Furthermore, because all the players were from the same team, it is unclear whether the results obtained would be generalizable to other teams and competitive levels. It should also be noted that the matches studied were not official competitions, because the Spanish Football Federation expressly prohibits players from wearing GPS devices. At all events, the differences observed in physical profile might be even greater if SGs were compared with competitive rather than FMs (19).
The results of this study provided evidence for the difference in activity patterns between SGs and FMs in male semiprofessional soccer players. Specifically, FMs showed higher demands in the high-intensity domain, questioning the original assumption of SG specificity.
Given this, attention should be paid when using SGs for training prescription because this training method failed to provide stress on activity variables deemed to potentially promote adaptations for the development of game repeated-sprint and repeated high-intensity activity. Indeed, SGs should be used preferably for the development of greater technical-tactical skills and for aerobic-fitness development.
With the aim to mimic FMs as closely as possible (i.e., high-intensity pattern), coaches have several options that may deem imposed variation in field dimensions, number of players, considered rules, and provided encouragement during SGs (31). Because of the interest in this issue for soccer training, further studies using the methods reported here are warranted.
This study is a part of the project entitled Avances Tecnológicos y Metodológicos en la Automatización de Estudios Observacionales en Deporte, funded by Spain's Dirección General de Investigación, Ministerio de Ciencia e Innovación (PSI2008-01179) over the period 2008–2011. In addition, the authors thank the Basque Country University (UPV-EHU) and the Department of Physical Education and Sport for providing the funding. No conflicts of interest exist for this research. This work was not supported by a funding source.
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